My interests as a statistician include methods to analyze high-dimensional, complex and potentially under-sampled regression and classification problems (in particular dimension reduction and feature selection methods); computational techniques for the empirical assessment of significance (e.g., re-sampling, perturbation and permutation schemes); latent structure and Markov modeling approaches; and functional data analysis methods.
Most of my applied research occurs at the interface between Statistics and contemporary “Omics” sciences. This work comprises interdisciplinary collaborations with biologists and computer scientists in which large genomic, epigenomic, transcriptomic, metagenomic (microbiomes) and metabolomic data sets are analyzed to investigate various aspects of genome dynamics, evolution and function – and to characterize human diseases.
In other interdisciplinary collaborations, I work on Meteorology applications where clustering and re-sampling techniques are used to improve forecast and delineate structure and lifecycle of tropical storms; on statistical analyses of the socioeconomic impacts of climate change; and on statistical methods for inference and emulation of agent-based models in Economics.
Over the years, my research has been supported by several awards from the National Institutes of Health, the National Science Foundation, the State of Pennsylvania, and the Pennsylvania State University.
Since 2016, I am a Fellow of the American Statistical Association “for outstanding collaborative work in high throughput biology, contributions to methodology in statistics and bioinformatics, commitment to interdisciplinary research, and leadership in developing training programs at the interface of statistics, computation and the life sciences.”